Content-based Filtering for Improving Movie Recommender System
Xinhua Tian · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2024
People constantly receive personalized information recommendations, and movie recommendation is one of the most recognized applications.Effective algorithms support the analysis of users' behavior, which helps to improve the rating system.Content-based filtering (CBF) is a major technique in recommender systems that operates on the premise of leveraging the relationship between user preferences and item characteristics to predict items.This paper provides a detailed look about the challenges that this method presents, emphasizing concerns with new users, inherent method limitations, issues with feature sparsity, the challenge of feature extraction, and the potential risk of over-specialization in suggestions.In synthesizing these challenges and innovations, this study highlights the potential of content-based filtering, marking its key role in the ongoing pursuit of personalized content delivery, while suggesting methods for improvement.